AI Pain Management Guidance for Personalized Trigger Analysis
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Solution Overview
Problem
Chronic pain is often overlooked and improperly treated, leading to negative impacts on patients' daily lives and increased risks of drug dependency and psychological disorders, with limited access to pain specialists and inadequate clinical guidelines.
Innovation Solution
A pain-management system using AI and neural networks to analyze individual user data, identify pain triggers and protectors, and provide personalized recommendations to manage chronic pain without face-to-face interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If chronic pain patients receive traditional treatment with limited access to pain specialists, then medical expertise can be provided, but patient accessibility and treatment coverage are insufficient
Solution Approach 1:
The system enables patients to self-manage their chronic pain through automated AI analysis of their activity data and generation of personalized recommendations, eliminating the need for face-to-face specialist interactions while maintaining personalized care
Solution Approach 2:
The patent replaces the mechanical system of in-person clinical consultations with an automated digital system using AI algorithms to analyze patient data and provide treatment recommendations, making pain management accessible remotely
2Measurement precision
If comprehensive patient monitoring and analysis systems are implemented, then treatment precision and personalization improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from patient activity data using the trained neural network, focusing on key patterns that predict pain levels while filtering out unnecessary information, thus achieving precise analysis without proportionally increasing system complexity
Solution Approach 2:
The neural network is pre-trained during a training phase using historical patient data, so that during actual use the system can quickly analyze new data without requiring complex real-time processing, as the pattern recognition capabilities are already established
3Productivity
If traditional face-to-face clinical interactions are required for pain management, then personalized care can be provided, but patient time consumption and accessibility are reduced
Solution Approach 1:
Patients continuously provide activity data through mobile devices and receive automated feedback and recommendations without scheduling appointments or traveling to clinics, eliminating time loss associated with traditional consultations while maintaining personalized care
Solution Approach 2:
The system provides continuous monitoring and feedback based on ongoing activity data collection, replacing discrete periodic consultations with continuous actionable insights that help patients manage pain in real-time throughout their daily lives
Data Source
AI summary
A pain-management system (300) configured to: receive a target-pain-score (314) that represents a level of pain that the user considers acceptable during a defined period of time: receive one or more target-input-parameters (304) which represent properties and/or activities of a user during the same defined period of time, wherein the one or more target-input-parameters (314) are a subset of a full list of input-parameters that are available: receive one or more settings (315) that represent one or more input-parameters that are to be increased or decreased: use a neural network that has been trained for the individual user to determine one or more calculated-user-parameters (316) based on the target-pain-score (314) and the target-input-parameters (304), wherein at least one of the calculated-user-parameters (316) is set based on the settings (315); and present the one or more calculated-user-parameters (316) using a user interface.